Journal of Distribution Science (유통과학연구)

Korea Distribution Science Association (한국유통과학회)
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How Customer Experience Management Can Improve the Distribution of Marketing Performance

  • Sri, SAFITRI (Management Department, Faculty of Economic and Business, Brawijaya University) ;
  • Achmad, SUDIRO (Management Department, Faculty of Economic and Business, Brawijaya University) ;
  • Fatchur, ROHMAN (Management Department, Faculty of Economic and Business, Brawijaya University) ;
  • Mugiono, MUGIONO (Management Department, Faculty of Economic and Business, Brawijaya University)
  • Received : 2022.10.15
  • Accepted : 2023.02.05
  • Published : 2023.02.28
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Abstract

Purpose: This study aims to determine the effect of Customer Experience Management (CEM) on the distribution of Marketing Performance mediated by Digital Business Innovation (DBI), Digital Operational Excellence (DOE), and Digital Value Co-Creation (DVC) in Indonesia Information & Technology (IT) industry. Research design, data, and methodology: This research was conducted in two stages, namely exploratory and explanatory on IT companies in Indonesia. The authors took 132 samples using proportionate random sampling method in three groups of companies. Results: The results showed that CEM had no significant positive effect on the distribution of Marketing Performance. CEM has no direct effect on the distribution of Marketing Performance through the mediation of DBI. CEM has direct effect on the distribution of Marketing Performance through the mediation of DOE and DVC. DBI has no significant effect in increasing the distribution of Marketing Performance. However, DOE and DVC have a significant effect in increasing the distribution of Marketing Performance. Conclusion: The distribution of Marketing Performance can be indicated better through CEM through mediation.

Keywords

1. Introduction

Digital transformation and advancement of marketing practices generate tremendous challenges for IT providers. Rapid changing market competition may lead to new competitors that might reduce market share of IT industry. These competitors take advantage of latest technological innovations to provide free services with a different business model from IT companies (Leeflang et al., 2014).

Digital disruption is also changing consumer behavior and purchasing decision-making patterns. This phenomenon is marked by the emergence of Service Providers without no network infrastructure called Over The Top (OTT) providers. They utilize network infrastructure built by telecommunication operators or Internet Service Providers (ISP), to provide value-added services with no charge.

The main players in the IT industry are telcos such as Telefonica, Vodaphone and Telkom Indonesia, which offer telephone services, short messaging service (SMS), 3G/4G/5G optical cable, and mobile infrastructure. These companies are competing with OTT providers, who provides various services such as WhatsApp, Facebook Messenger, search engines, social media, and others. These services have a huge impact on the IT industry, including decline in revenue from calls and SMS which poses a threat to the IT industry who has made large investments on network infrastructure, while OTT providers still dominating most of the revenue.

According to Frisiani et al. (2017), declining marketing performance was marked by a decreasing of revenue growth and increasing of capital expenditure by 15% from 2010 to 2015. The decrease happened due to significant increase in internet traffic, which requires additional international bandwidth investments, without being followed by an equivalent increase in revenue, which has an impact on decreasing profitability.

Based on pilot research, the question of this research is whether there is an influence of Customer Experience Management (CEM) to improve the distribution of Marketing Performance. The distribution of Marketing performance can be grouped and measured by the following indicators and items:

[1] Financial performance: which includes sales volume, profitability, Return on Investment (ROI) which tends to decline due to competition from the OTT industry and the development of new technology in the digital era.

[2] Non-financial performance: which includes customer retention, customer satisfaction and loyalty, which are not yet fully a priority for this industry and need to be optimized in the changing digital transformation.

Experts generally ignore the impact of managing customer experience on the distribution of Marketing Performance. According to (Mihardjo et al., 2019), customer experience orientation is not proven to affect transformational performance when co-creation elements are included in Marketing Performance. Economic outcomes that may be counterproductive for service providers following the concept of co-creation including market performance (Oertzen et al., 2018). According to (Zaid, 2021) customer experience has no significant effect on customer loyalty and customer satisfaction. This creates a research gap to find out the true relationship between Customer Experience Management (CEM) that can affect to improve the distribution of marketing performance (Ying et al., 2020; Kim, 2005).

The definition of customer experience (CX), variables and constructs, and performance measurement are varied across studies. Customer experience closely related with the relationship between customers and service providers (Gentile et al., 2007). CEM also affects direct marketing, supply chain and business performance (Mustikasari et al., 2021). The previous study suggests that Customer Experience Management (CEM) is positively linked to financial performance; this effect increment in the intensity of competition, market turbulence, and technological turbulence. Customer experience appeals to practitioners may improve financial performance through long-term customer loyalty. Therefore, the researchers chose Customer Experience Management (CEM) as a determinant that affects the distribution of Marketing Performance over other determinants that might influence it (Klink et al., 2020).

The perspective of mediating variables is supported in the Forrester report which concludes that Digital Operational Excellence (DOE) increases business agility (Gill, 2015). Companies in this modern era are concerned with internal efficiency and progress in Digital Operational Excellence (DOE) in their fast-changing industry. While Digital Operational Excellence (DOE) is used as a mediating variable to support the influence of Customer Experience Management (CEM) on the distribution of Marketing Performance, an empirical study shows that the Digital Operational Excellence (DOE) process initiative has a strong internal focus, leaving customer experience problems far behind (Moore, 2015). Previous research (Farias et al., 2014) shows that the adjustment of operational activities has a significant impact in the perceived value of a marketing activity. Since there is no research that specifically discusses the Digital Operation Excellence variable, this research aims to fill that void.

This study also positions Digital Value Co-Creation (DVC) as the last mediating variable to support the impact of Customer Experience Management (CEM) on the distribution of Marketing Performance. Previous study stated that companies should focus on allowing customers as part of value co-creation to actively participate in building a profitable customer experience, which will positively impact the delivery of customer experience value. Since there is no research that discusses the influence between Customer Experience Management (CEM) and Digital Value Co-Creation (DVC), this research aims to fill that void.

Thus, this study was conducted to develop a new conceptual model and bridge the gap by including Digital Business Innovation (DBI), Digital Operational Excellence (DOE), and Digital Value Co-Creation (DVC) as mediation to improve the distribution of Marketing Performance. This model was tested empirically on an IT industry company that involves the C-level management position or the executive-level and above (Assistant Vice President, Senior Manager, General Manager, Director, Vice President or Group Head).

2. Literature Review

2.1. Marketing Performance

A study of principles of marketing (Kotler & Armstrong, 2010) defines marketing as the process by which companies create value for customers and build strong interactions to capture value from customers in return. In this paper, marketing is defined as an individual and organizational activity designed to create, communicate, and deliver socio-economic value requirements for relevant stakeholders, and manage innovation to remain competitive in a dynamic business environment. Marketing is still absolutely an exceptional function of the company, and no successful business is possible to improve marketing performance without effective marketing.

2.2. Customer Experience Management (CEM)

Customer experience (CX) can be described as "a multidimensional construct that focuses on the customer's cognitive, emotional, behavioral, sensory, and social responses to company's offers and actions (Lemon & Verhoef, 2016). Companies increasingly see Customer Experience Management (CEM) as the main source of competitive advantage and specifically as a strategic response to commoditization. When offers becomes a commodity, product leadership and operational excellence become less influential, while close customer relationships become critical. In order to meet customers’ needs companies need to design an impressive customer experiences (Witell et al., 2020).

2.3. Digital Business Innovation (DBI)

Business transformation is basically a change management strategy with a primary focus in positioning people, processes, and technology initiatives of the business enterprise. The study of the company's perspective that includes people, processes, and systems or technology (tools) has been used long before the Digital Business Innovation (DBI) study existed (Euchner & Ganguly, 2014; Frankenberger et al., 2013). The previous research has found that contemporary studies of companies and organizational studies are more related to business innovation (Trimi & Berbegal-Mirabent, 2012; Spithoven et al., 2013).

2.4. Digital Operation Excellence (DOE)

Digital Operation Excellence (DOE) relies on implementing a predetermined digital transformation strategy. The systematic transformation process aims to achieve continuous performance improvements that are in line with the objectives. Digital operations can optimize asset utilization and employee productivity. In this study, the results of implementing operational excellence are focused on operational performance and organizational sustainability performance (Fok-Yew, 2014).

2.5. Digital Value Co-Creation (DVC)

This study will build a conceptual model that includes aspects of Customer Experience Management, which are influenced by three mediation indicators of Digital Operational Excellence formed by business process reengineering, decision support systems, and total quality management.

The success of the distribution of Marketing Performance is also inseparable from the influence of the mediation aspect of Digital Business Innovation formed by innovation and Digital Value Co-Creation which is formed by the indicators of co-production and value-in-use. The relationship between variables has been summarized on (see Table 1).

Table 1: The Main Contents of Previous Study

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3. Research Methods and Materials

This research carried out in exploratory and explanatory objectives. Based on Sekaran and Bougie (2016), this research uses mixed approach; qualitative and quantitative. The study is being conducted on IT companies in Indonesia, with most of them based in the Jabodetabek area (Greater Jakarta), which has the most competition in the IT industry. This research data was gathered through virtual or in-person meetings with respondents.

Respondent responses were measured on a 5-point Likert Scale from 1 (strongly disagree) to 5 (strongly agree). This research started in October 2021 until its completion. The research concept framework as shown in (see Figure 1).

OTGHB7_2023_v21n2_53_f0001.png 이미지

Figure 1: Research Concept Framework

H1: Customer Experience Management (CEM) has a significant influence on the distribution of Marketing Performance

H2: Customer Experience Management (CEM) has a significant influence on the distribution of Marketing Performance through Digital Business Innovation (DBI).

H3: Customer Experience Management (CEM) has a significant influence on the distribution of Marketing Performance through Digital Operational Excellence (DOE).

H4: Customer Experience Management (CEM) has a significant influence on the distribution of Marketing Performance through Digital Value Co-Creation (DVC)

H5: Digital Business Innovation (DBI) has significant influence on the distribution of Marketing Performance

H6: Digital Operational Excellence (DOE) has a significant influence on the distribution of Marketing Performance

H7: Digital Value Co-Creation (DVC) have a significant influence on the distribution of Marketing Performance

4. Result and Discussion

The discussion of the exploratory study includes the results of the Focus Group Discussion (FGD) to explore the indicators and items of each research variable. Researchers tested the instrument (validity and reliability) after distributing questionnaires to the first 30 respondents. Furthermore, the researchers conducted explanatory tests and hypothesis testing, before conducted a final FGD to obtain input from practitioners in the IT industry based on the results of the research hypothesis testing.

4.1. Exploratory Study Results

Researchers conducted FGDs with three groups, which consists of industry leaders, regulators and academics who meet the research respondents’ criteria. The criteria of the respondents are (1) having a doctoral education, (2) working in national IT companies, (3) holding senior managerial positions. Participants in the FGD came from companies and institutions that have implemented digital technolog.

The FGD was held to elicit the views and opinions of participants who were thought to represent the population in Indonesia's IT industry. Researchers through the FGD, received statements that could strengthen statement items or indicators on the variables of Customer Experience Management, Digital Business Innovation, Digital Operational Excellence, Digital Value Co-Creation and Marketing Performance adopted from several researchers (Kotler & Armstrong, 2010; Venkatesh & Singhal, 2018; Jaeger et al., 2014; Ranjan and Read., 2016; Mustafa, 20090. The FGD produced new statement items or indicators that are more relevant to Indonesia, which were then tested using Confirmatory Factor Analysis (CFA) to define the validity and reliability of each statement item so ensuring that the indicators formed were able to measure the latent variables tested in this study.

4.2. Explanatory Study Results

This study employs a variance-based or component-based approach model with the Partial Least Square (PLS) method. The PLS approach is used to test the results of Structural Equation Modeling (SEM), which was done by looking at the results of the measurement model (outer model) and the results of the structural model (inner model) of the model under study. The outer model evaluation seeks to examine the relationship between items and latent variables, whereas the inner model evaluation seeks to examine the relationship between latent variables.

4.3. Measurement Model

The results of the path diagram for the outer loading value of each statement item on the indicators and variables (see Figure 2). Furthermore, the validity results will be explained based on the outer loading value of each variable in the second order analysis.

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Figure 2: Measurement Model Diagram

Items in each variable can be considered valid if the value of the outer loading indicator is more than 0.7. Based on the outer loading and AVE values obtained through the SEM-PLS Algorithm, it is found that each statement item is greater than 0.7 so it is considered valid in measuring indicators and variables. Since the results of the outer model test concluded that all variables and item constructed were valid and reliable, therefore, each item could be used for further analysis.

4.4. Structural Model

The inner model analysis was conducted to assess the relationship between the latent variable constructs. In this study, the values of predictive - relevance (Q2) and R-square (R2) for the latent endogenous construct were examined to assess the accuracy of the parameter estimates and observed values generated by the inner model.

The results of these calculations (see Table 2) indicate that the R-square value in this research model can be classified as strong or moderately strong. Hence, implying that the variables in the model have a greater influence on the dependent variable than other factors originating outside the research model. Meanwhile, the Q2 calculation result in this study is 0.961, indicating that the model in this study has a relevant predictive value.

Table 2: Evaluation of Inner Model

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In this section, the authors elaborate the results of the research hypothesis testing, specifically the test of direct and indirect effects (mediation) between latent variables. Hypothesis testing in PLS is done by bootstrapping the sample. Multigroup analysis as applied using the partial least squares structural equation model (PLS-SEM) is a way to test preselected data sets to determine if there are significant differences in the estimates of group-specific parameters. This method, as implemented in Smart PLS, is an extension of the bootstrap-based multigroup analysis approach originally proposed for PLS-SEM (Henseler et al., 2009).

Based on Henseler et al. (2009), there are two models of analysis involving mediator variables:

[1] Full Mediation, which means that the independent variable is not able to influence the dependent variable significantly without going through the mediator variable.

[2] Part Mediation, which means that the independent variable can influence the dependent variable directly without going through/involving the mediator variable.

The results of the inner model evaluation based on the relationship between constructs (see Table 3). If the t-value is greater than t-table (1.96), it indicates that there is a significant influence on the relationship between the constructs of the analyzed latent variables.

Table 3: Evaluation of Inner Model

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Hypothesis 1: The Effect of Customer Experience Management (CEM) on the Distribution of Marketing Performance (MP)

The t-statistic value on the CEM path to Marketing Performance is 0.466 (see Table 3). This indicates that the t-statistic has less value than the t-table (1.96). Consequently, according to the result, hypothesis 1 is rejected. It means that CEM has no significant effect on the distribution of Marketing Performance. The path coefficient value is 0.054, which indicates a positive relationship. A positive relationship means an increase in CEM that affect increment in MP, but the effect is not significant.

The test results are in accordance with Mihardjo et al. (2019) research, which states that customer experience orientation has no effect in increasing transformational performance in the IT industry in Indonesia.

Similar results are also found in the research of Pramudika and Wickramasooriya (2016) which states that although there is a relationship between CEM and market performance, there is no relationship between CEM and financial performance in the case study of Sri Lankan printing & publishing industry (Colombo Region). This makes it somewhat difficult to interpret whether there is a relationship between CEM and business performance in general, because business performance consists of market performance and financial performance. In this study, it is stated that financial performance is highly influenced by political factors and economic factors rather than CEM strategies. Meanwhile, financial performance is one of the indicators used to measure the marketing performance variables on the IT industry in Indonesia.

Other results can be seen in Chandra’s (2014) research, which states that the results of this study fail to prove that customer experience has a significance for customer loyalty through customer satisfaction mediation at Ciputra World Surabaya. The ineffectiveness of CEM on the performance of a company, which includes financial performance, marketing performance, business performance, and other performance, might be caused by various factors that also influence the scope of a company or industry.

Hypothesis 2: The Influence of Customer Experience Management (CEM) on the Distribution of Marketing Performance (MP) through Digital Business Innovation (DBI)

The t-statistic value on the CEM path towards DBI towards the distribution of Marketing Performance of 0.009 is smaller than the t-table value (1.96) (see Table 3). Therefore, hypothesis 2 is rejected. It shows that CEM through mediation variables DBI has no significant effect on the distribution of Marketing Performance. The path coefficient value is 0.001, which means that an increase in CEM can increase DBI. It indicates a direction of a positive relationship. This positive relationship might increase the distribution of MP, but the effect is not significant. It shows that the relationship between constructs is a “partial mediation” model (Henseler et al., 2009), meaning that the independent variable (CEM) can directly influence the dependent variable (MP) without involving/going to the mediator variable, DBI.

As mentioned in the results of Hypothesis 1, the results of CEM have no significant effect on the distribution of marketing performance. Furthermore, according to Mihardjo et al.’s (2019) research, states that customer experience orientation does not affect increasing transformational performance in the IT industry in Indonesia. In addition, Susdiani (2020) states that process innovation does not affect any type of MSME performance in the creative industry in Padang City. In this study, it was found that organizational innovation influences all types of performance, namely financial performance, consumer performance, internal business process performance, and learning and growth performance. In contrast, process innovation does not have a significant effect on all types of performance. The results of this study indicate that not every innovation can have a positive and significant impact on performance.

Based on research by Mihardjo et al. (2019) and Susdiani (2020) show that customer experience and innovation do not always have a significant effect on improving performance. This is due to other factors outside of customer experience and innovation which also have an impact on the decline or increase in company performance.

Hypothesis 3: The Influence of Customer Experience Management (CEM) on the Distribution of Marketing Performance (MP) through Digital Operational Excellence (DOE)

The value of t-statistics on the path of CEM towards DOE towards Marketing Performance of 2.298 is greater than the value of t-table (1.96) (see Table 3), so it can be concluded that the Hypothesis 3 is accepted, meaning that CEM through DOE has a significant effect on the distribution of Marketing Performance. The path coefficient value is 0.290 which indicates the direction of a positive relationship, which means that increasing CEM will cause an increase in DOE which in turn causes an increase in the distribution of MP. This shows that the relationship between constructs is a “full mediation” model (Henseler et al., 2009), meaning that the independent variable (CEM) is not able to significantly influence the dependent variable (MP) without going through the mediator variable (DOE).

The results of this study are also following Makudza’s (2020) research in a case study of the banking industry. Around 2010, retail banks in Africa had improved their customer service beyond customer expectations indicating that the move was to increase customer satisfaction and customer loyalty. Good management of this digital platform acts as an antecedent of CEM and thereby increases customer loyalty. Based on this, the researcher sees that good digital platform management is one of the DOE efforts to improve customer experience and company performance.

Hypothesis 4: The Effect of Customer Experience Management (CEM) on the Distribution of Marketing Performance (MP) through Digital Value Co-Creation (DVC)

The value of t-statistics on the CEM path to DVC on the distribution of Marketing Performance is 3,273 greater than the value of t-table (1.96) (see Table 3), so it can be concluded that hypothesis 4 is accepted, meaning that CEM through variable DVC mediation has a significant effect on the distribution of Marketing Performance. The path coefficient value is 0.244 which indicates the direction of a positive relationship, which means that increasing CEM will cause an increase in DVC which in turn causes an increase in the distribution of Marketing Performance. This shows that the relationship between constructs is a “full mediation” model (Henseler et al., 2009), meaning that the independent variable (CEM) is not able to significantly influence the dependent variable (MP) without going through the mediator variable (DVC).

The results of this study are in accordance with Mihardjo et al. (2019), which states that customer experience orientation influences increasing co-creation strategy, and co-creation strategy affects transformational performance in the IT industry in Indonesia. In this study, the co-creation strategy was built by vision, collaboration sharing, and benefits value.

Hypothesis 5: Effect of Digital Business Innovation (DBI) on the Distribution of Marketing Performance (MP)

The t-statistic value on the DBI path toward the distribution of Marketing Performance directly is 0.009 smaller than the t-table value (1.96) (see Table 3). Therefore, it can be concluded that hypothesis 5 is rejected, meaning that DBI has no significant effect on the distribution of Marketing Performance. The path coefficient value is 0.001 which indicates the direction of a positive relationship, which means that increasing DBI will cause an increase in the distribution of Marketing Performance, but the effect is not significant.

The results of this study are not in accordance with Mihardjo et al. (2019), which states that the business model innovation built by structure innovation, content innovation, commercial innovation, and governance innovation delivery influences transformational performance in the IT industry in Indonesia. In this study, business model innovation is also correlated with co-creation strategy.

Similar findings to the results of this study were found in the study of Kusuma et al., 2021 in a case study at PT. INKA (Railway Industry) Madiun. In this research, it is known that PT. INKA Madiun has launched the INKA Mobile application to make it easier for employees to complete their tasks, so this research is focused on explaining the impact of innovation on employee performance with self-efficacy as a moderating variable. From this study, it was found that innovation did not have a significant positive effect on performance, while self-efficacy had a positive effect on employee performance. Through the moderation test, it was found that self-efficacy weakens the relationship between innovation and performance.

Hypothesis 6: The Effect of Digital Operational Excellence (DOE) on the Distribution of Marketing Performance (MP)

The t-statistic value on the DOE path to the distribution of Marketing Performance directly is 2,413 greater than the t-table value (1.96) (see Table 3). Therefore, it can be concluded that hypothesis 6 is accepted, meaning that DOE has a significant effect on the distribution of Marketing Performance. The path coefficient value is 0.388 which indicates the direction of the positive relationship, which means that increasing DOE will cause an increase in the distribution of MP.

The results of this study are in accordance with the study of Ojha (2015), which states that operational excellence has a greater impact on customer retention which is a marketing performance variable. Meanwhile, different findings are found in Mihardjo et al. (2019) where the distinctive operational capabilities built by digital leadership values, culture leaders, high technology assets, and adaptation to environmental agility have no effect in increasing transformational performance in the IT industry in Indonesia.

Similar results were also found in the research by Alexieva et al. (2018) which stated that innovativeness, creativity, business alertness, and risk-taking had a significant effect on the successful implementation of Business Process Management (BPM) and affected the performance of Bulgarian companies. The study concludes that management mindset or lack of it has a major influence on business performance and if any organization leans towards development and growth should embrace the concept of BPM.

Hypothesis 7: The Effect of Digital Value Co-Creation (DVC) on the Distribution of Marketing Performance (MP)

The t-statistic value on the DVC path to the distribution of Marketing Performance directly is 3,353 greater than the t-table value (1.96) (see Table 3). Therefore, it can be concluded that hypothesis 7 is accepted, meaning that DVC has a significant effect on the distribution of Marketing Performance. The path coefficient value is 0.385 which indicates the direction of the positive relationship, which means that increasing DVC will cause an increase in the distribution of MP.

The results of this study are in accordance with Mihardjo et al. (2019), which states that the co-creation strategy built by vision, collaboration sharing, and benefits value affects transformational performance in the IT industry in Indonesia. Benefit value has the biggest influence in improving transformational performance, followed by collaboration sharing and vision.

The article written by Indriastuti (2019) also discusses the important role of shared value creation, where in the journal it is stated that these indicators bridge the gaps that exist in marketing performance. The impact affects the relationship with customers and business performance.

Meanwhile, similar results related to the influence of DBI on the distribution of Marketing Performance are found in the research of Susdiani (2020), which states that process innovation does not affect any type of MSME performance in the creative industry in Padang City and the research of Kusuma et al (2021) which states that innovation of mobile application PT. INKA does not have a significant positive effect on performance. Several previous studies that had similar results to this study occurred in Indonesia and Sri Lanka, where research conducted in Indonesia included case studies on the IT industry (Mihardjo et al., 2019), Ciputra World Surabaya (Chandra, 2014), the creative industry in the city of Padang (Susdiani, 2020), and PT. INKA Madiun (Kusuma et al., 2021).

Several previous studies that had different results from this study came from studies outside Indonesia, including research from the Department of Marketing at Loyola University Maryland USA by Klink et al. (2020) in the European Journal of Marketing which stated that there was a positive influence from CEM on financial performance. Another research made by Gronholdt et al. (2015) states that there is a positive and significant effect of CEM on market performance as measured by customer-perceived product quality, service quality, customer satisfaction, customer loyalty, number of new customers and corporate image (a case study of a company in Denmark)

Researchers see a relationship between the research country and the research results obtained, where it appears that research in Indonesia and Sri Lanka showed similar results to this study. While research outside Indonesia such as in the USA, Denmark, and Italy showed that there was a positive influence of CEM and innovation on company performance. This gives the assumption that a country's digital adaptability affects the implementation of CEM and DBI in a company. Developed countries such as the USA, Denmark, and Italy have better economic capacity to adopt and explore digital technologies that lead to transformations in government practices, business models, and society in general compared to Indonesia and Sri Lanka.

The novelty generated through the minor analysis in this study is proof of whether the mediating factor affects the dependent variable which is the distribution of MP in this study or whether there are other factors. The results found in this study can be used as an alternative solution model to improve the distribution of Marketing Performance, as well as used as a basis for consideration for the world of practitioners to thrive in a competitive global market and to solve current problems faced by companies in this digital age.

5. Conclusion

5.1. Summary

This study aims to determine the impact of Customer Experience Management on the distribution of Marketing Performance mediated by Digital Business Innovation, Digital Operational Excellence, and Digital Value CoCreation in the IT Industry in Indonesia. From the analysis, it can be concluded that CEM has a low contribution to the distribution of Marketing Performance without being assisted by other factors.

This result makes it somewhat difficult to interpret since the contribution of CEM towards business performance seems unclear. In this study, it is stated that the influence of political factors and economic factors directly affects business performance rather than CEM strategies when considering their financial performance. Meanwhile, financial performance is one of the values used to measure the marketing performance in this research on the IT industry in Indonesia. Therefore, it is crucial to have clear data and interpretation to help measure the distribution of marketing performance.

This study found that CEM has a significant effect on the distribution of Marketing Performance through the mediation of Digital Operational Excellence and Digital Value Co-Creation. On the other hand, CEM has no significant effect on the distribution of marketing performance through the mediation of Digital Business Innovation. Furthermore, this study found that DBI has no significant effect in increasing the distribution of marketing performance. However, DOE and DVC have a significant effect in increasing the distribution of Marketing Performance.

5.2. Implications

The author hope that this study can contribute to the development of theoretical knowledge, especially in the field of marketing management. Furthermore, the finding of this research as a reference for managers and policy makers in public organizations to set the most effective way to enhance Customer Experience Management and the distribution of Marketing Performance, especially through Digital Business Innovation, Digital Operational Excellence, and Digital Value Co-Creation.

5.3. Limitation

The limitation in this study is the researchers only discussed the topic in specific industry which is IT industry in Indonesia. Given the limitations of this study, the authors suggests that future researchers use the models mentioned in this study as a reference to improve the distribution of Marketing Performance in various industries and sectors. Furthermore, insignificant results in this study can be used as a consideration for future research to investigate the relationship between Customer Experience Management and business performance in general more profoundly, resulting in a more definite theory. Finally, because the current study used a quantitative approach, it would be interesting to conduct a qualitative study or mixed method research to investigate how Customer Experience Management contributes to the distribution of marketing management, whether through external or internal factors.

References

  1. Chandra, S. (2014). The impact of customer experience toward customer satisfaction and loyalty of Ciputra World Surabaya. iBuss Management, 2(2), 1-11.
  2. Euchner, J., & Ganguly, A. (2014). Business model innovation in practice: A systematic approach to business model innovation can help capture value and reduce risks. Research Technology Management, 57(6), 33-39. doi.org/10.5437/08956308X5706013
  3. Farias, S. A. de, Aguiar, E. C., & Melo, F. V. S. (2014). Store atmospherics and experiential marketing: A conceptual framework and research propositions for an extraordinary customer experience. International Business Research, 7(2). doi.org/10.5539/ibr.v7n2p87
  4. Fok-Yew, O. (2014). The effect of change management on operational excellence in electrical and electronics industry: Evidence from Malaysia. British Journal of Economics, Management & Trade, 4(8), 1285-1305. doi.org/10.9734/bjemt/2014/9201
  5. Foroudi, P., Jin, Z., Gupta, S., Melewar, T. C., & Foroudi, M. M. (2016). Influence of innovation capability and customer experience on reputation and loyalty. Journal of Business Research, 69(11), 4882-4889. doi.org/10.1016/j.jbusres.2016.04.047
  6. Frankenberger, K., Weiblen, T., Csik, M., & Gassmann, O. (2013). The 4I-framework of business model innovation: A structured view on process phases and challenges. International Journal of Product Development, 18(3-4), 249-273. doi.org/10.1504/IJPD.2013.055012
  7. Frisiani, G., Jay J., Lajous, T., & Natterman, P. (2017). A future for mobile operators: The keys to successful reinvention. McKinsey&Company.
  8. Gentile, C., Spiller, N., & Noci, G. (2007). How to sustain the customer experience: An overview of experience components that co-create value with the customer. European Management Journal, 25(5), 395-410. doi.org/10.1016/j.emj.2007.08.005
  9. Gill, M. (2015). Don't Build A Digital Strategy; Digitize Your Business Strategy. Forrester.
  10. Gronholdt, L., Martensen, A., Jorgensen, S., & Jensen, P. (2015). Customer experience management and business performance. International Journal of Quality and Service Sciences, 7(1), 90-106. doi.org/10.1108/IJQSS-01-2015-0008
  11. Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. New Challenges to International Marketing: Advances in International Marketing, 20, 277-319. doi.org/10.1108/S1474-7979(2009)0000020014
  12. Indriastuti, H. (2019). Entrepreneurial innovativeness, relational capabilities, and value co-creation to enhance marketing performance. Humanities and Social Sciences Reviews, 7(3), 181-188. doi.org/10.18510/hssr.2019.7328
  13. Kim, H. R. (2005). Developing an index of online customer satisfaction. Journal of Financial Service Marketing, 10(1), 49-64. doi.org/10.1057/palgrave.fsm.4770173
  14. Klink, R. R., Zhang, J. Q., & Athaide, G. A. (2020). Measuring customer experience management and its impact on financial performance. European Journal of Marketing, 55(3), 840-867. doi.org/10.1108/EJM-07-2019-0592
  15. Kotler, P.; Armstrong, G. (2010). Principles of Marketing. 13th Edition. Pearson Prentice Hall.
  16. Leeflang, P. S. H., Verhoef, P. C., Dahlstrom, P., & Freundt, T. (2014). Challenges and solutions for marketing in a digital era. European Management Journal, 32(1), 1-12. https://doi.org/10.1016/j.emj.2013.12.001
  17. Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69-96. doi.org/10.1509/jm.15.0420
  18. Loonam, J., Eaves, S., Kumar, V., & Parry, G. (2018). Towards digital transformation: Lessons learned from traditional organizations. Strategic Change, 27(2), 101-109. doi.org/10.1002/jsc.2185
  19. Makudza, F. (2020). Augmenting customer loyalty through customer experience management in the banking industry. Journal of Asian Business and Economic Studies, 28(3), 191-203. doi.org/10.1108/jabes-01-2020-0007
  20. Mihardjo, L. W. W., Sasmoko, F. A., & Elidjen. (2019). The influence of digital customer experience and electronic word of mouth on brand image and supply chain sustainable performance. Uncertain Supply Chain Management, 7(4), 691-702. doi.org/10.5267/j.uscm.2019.4.001
  21. Mihardjo, L. W. W., Sasmoko, F. A., & Elidjen. (2019). Towards co-creation strategy and organizational agility based on customer experience orientation to shape transformational performance. International Journal of Innovation, Creativity and Change, 6(1), 236-248.
  22. Moore, B. C. (2015). Transform Customer Experience and Operational Excellence By Going Digital Outside and Inside Organizations Struggle. Digital Clarity Group
  23. Mustikasari, A., Krisnawati, M., & Sutrisno, E. (2021). Customer experience and repurchase intention in multi-channel: Customer satisfaction as mediating variable. The Journal of Industrial Distribution & Business, 12(3), 7-19. doi.org/10.13106/jidb.2021.vol12.no3.7
  24. Nambisan, S. (2002). Designing virtual customer environments for new product development: Toward a theory. Academy of Management Review, 27(3), 392-413. doi.org/10.5465/AMR.2002.7389914
  25. Oertzen, A. S., Odekerken-Schroder, G., Brax, S. A., & Mager, B. (2018). Co-creating services-conceptual clarification, forms and outcomes. Journal of Service Management, 29(4), 641-679. doi.org/10.1108/JOSM-03-2017-0067
  26. Ojha, S. K. (2015). Operational excellence for sustainability of Nepalese industries. Procedia - Social and Behavioral Sciences, 189, 458-464. doi.org/10.1016/j.sbspro.2015.03.196
  27. Prahalad, C. K., & Ramaswamy, V. (2004). Co-creation experiences: The next practice in value creation. Journal of Interactive Marketing, 18(3), 5-14. doi.org/10.1002/dir.20015
  28. Ramaswamy, V. (2011). It's about human experiences...and beyond, to co-creation. Industrial Marketing Management, 40(2), 195-196. doi.org/10.1016/j.indmarman.2010.06.030
  29. Ranjan, K. R., & Read, S. (2016). Value co-creation: Concept and measurement. Journal of the Academy of Marketing Science, 44(3), 290-315. doi.org/10.1007/s11747-014-0397-2
  30. Saunila, M., Ukko, J., & Rantala, T. (2019). Value co-creation through digital service capabilities: the role of human factors. Information Technology and People, 32(3), 627-645. doi.org/10.1108/ITP-10-2016-0224
  31. Sekaran, U., & Bougie, R. (2016). Research Methods for Business: a skill-building Approach, 7th Ed. (7th). Chichester: John Wiley & Sons. Inc.
  32. Spithoven, A., Vanhaverbeke, W., & Roijakkers, N. (2013). Open innovation practices in SMEs and large enterprises. Small Business Economics, 41(3), 537-562. doi.org/10.1007/s11187-012-9453-9
  33. Tekic, Z., & Koroteev, D. (2019). From disruptively digital to proudly analog: A holistic typology of digital transformation strategies. Business Horizons, 62(6), 683-693. doi.org/10.1016/j.bushor.2019.07.002
  34. Tivasuradej, Y. C. T., & Pham, N. (2019). Advancing customer experience practice and strategy in Thailand. Asia Pacific Journal of Marketing and Logistics, 31(2), 327-343. doi.org/10.1108/APJML-09-2017-0220
  35. Trimi, S., & Berbegal-Mirabent, J. (2012). Business model innovation in entrepreneurship. International Entrepreneurship and Management Journal, 8(4), 449-465. doi.org/10.1007/s11365-012-0234-3
  36. Witell, L., Kowalkowski, C., Perks, H., Raddats, C., Schwabe, M., Benedettini, O., & Burton, J. (2020). Characterizing customer experience management in business markets. Journal of Business Research, 116, 420-430. doi.org/10.1016/j.jbusres.2019.08.050
  37. Ying, S., Sindakis, S., Aggarwal, S., Chen, C., Su, J. (2020). Managing big data in the retail industry of Singapore: Examining the impact on customer satisfaction and organizational performance. European Management Journal, 39(3), 390-400. doi.org/10.1016/j.emj.2020.04.001
  38. Zaid, S., & Patwayati, P. (2021). Impact of customer experience and customer engagement on satisfaction and loyalty: A case study in Indonesia. The Journal of Asian Finance, Economics and Business, 8(4), 983-992. doi.org/10.13106/JAFEB.2021.VOL8.NO4.0983